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Published on June 20, 2007

Author: Arundel0


A Search for nmne Oscillation with MiniBooNE:  A Search for nmne Oscillation with MiniBooNE Hai-Jun Yang University of Michigan, Ann Arbor (on behalf of MiniBooNE Collaboration) The 6th KEK Topical Conference Frontiers in Particle Physics and Cosmology KEK, Tsukuba, Japan, February 6-8, 2007 Outline:  Outline Physics Motivation The MiniBooNE Experiment Neutrino Beam Flux Event Reconstruction andamp; Identification NuMI / MiniBooNE Data vs. MC Measurement of Dirt Events Expected Neutrino Oscillation Result The LSND Experiment:  The LSND Experiment Oscillations? Signal: p  e+ n n p  d (2.2MeV) LSND took data from 1993-98 Nearly 49000 Coulombs of protons on target Baseline: 30 meters Neutrino Energy: 20-55 MeV LSND Detector: -- 1280 phototubes -- 167 tons Liquid Scintillator Observe an excess ofe : -- 87.9 ± 22.4 ± 6.0 events. The LSND Experiment:   LSND observed a positive signal(~3.8s), but not confirmed. The LSND Experiment Physics Motivation:  Physics Motivation  If the LSND signal does exist, it will imply new physics beyond SM.  The MiniBooNE is designed to confirm or refute LSND oscillation result at Dm2 ~ 1.0 eV2 . Dm2atm + Dm2sol ≠ Dm2lsnd K2K, Minos How can there be 3 distinct Dm2 ?:  How can there be 3 distinct Dm2 ? Mass Difference Equation: (m12 –m22) + (m22-m32) = (m12 –m32) One of the experimental measurements is wrong One of the experimental measurements is not neutrino oscillations:  Neutrino decay  Neutrino production from flavor violating decays Additional 'sterile' neutrinos involved in oscillation CPT violation or CP violation + sterile n’s allows different mixing for n’s and n bars. The MiniBooNE Experiment:  The MiniBooNE Experiment Proposed in summer 1997,operating since 2002 The goal of the MiniBooNE Expriment: to confirm or exclude the LSND result and extend the explored oscillation parameter space Similar L/E as LSND Baseline: L = 451 meters, ~ x15 LSND Neutrino Beam Energy: E ~ x(10-20) LSND Different systematics: event signatures and backgrounds different from LSND High statistics: ~ x5 LSND Expected ~ 90% C.L. for most of LSND allowed region The MiniBooNE Collaboration:  Y.Liu, D.Perevalov, I.Stancu University of Alabama S.Koutsoliotas Bucknell University R.A.Johnson, J.L.Raaf University of Cincinnati T.Hart, R.H.Nelson, M.Tzanov M.Wilking, E.D.Zimmerman University of Colorado A.A.Aguilar-Arevalo, L.Bugel L.Coney, J.M.Conrad, Z. Djurcic, K.B.M.Mahn, J.Monroe, D.Schmitz M.H.Shaevitz, M.Sorel, G.P.Zeller Columbia University D.Smith Embry Riddle Aeronautical University L.Bartoszek, C.Bhat, S.J.Brice B.C.Brown, D. A. Finley, R.Ford, F.G.Garcia, P.Kasper, T.Kobilarcik, I.Kourbanis, A.Malensek, W.Marsh, P.Martin, F.Mills, C.Moore, E.Prebys, A.D.Russell , P.Spentzouris, R.J.Stefanski, T.Williams Fermi National Accelerator Laboratory D.C.Cox, T.Katori, H.Meyer, C.C.Polly R.Tayloe Indiana University G.T.Garvey, A.Green, C.Green, W.C.Louis, G.McGregor, S.McKenney G.B.Mills, H.Ray, V.Sandberg, B.Sapp, R.Schirato, R.Van de Water N.L.Walbridge, D.H.White Los Alamos National Laboratory R.Imlay, W.Metcalf, S.Ouedraogo, M.O.Wascko Louisiana State University J.Cao, Y.Liu, B.P.Roe, H.J.Yang University of Michigan A.O.Bazarko, P.D.Meyers, R.B.Patterson, F.C.Shoemaker, H.A.Tanaka Princeton University P.Nienaber Saint Mary's University of Minnesota J. M. Link Virginia Polytechnic Institute and State University E.Hawker Western Illinois University A.Curioni, B.T.Fleming Yale University The MiniBooNE Collaboration Fermilab Booster:  Fermilab Booster MiniBooNE The MiniBooNE Experiment:  The MiniBooNE Experiment The FNAL Booster delivers 8 GeV protons to the MiniBooNE beamline. The protons hit a 71cm beryllium target producing pions and kaons. The magnetic horn focuses the secondary particles towards the detector. The mesons decay into neutrinos, and the neutrinos fly to the detector, all other secondary particles are absorbed by absorber and 450 m dirt. 5.579E20 POT for neutrino mode since 2002. Switch horn polarity to run anti-neutrino mode since January 2006. MiniBooNE Flux:  MiniBooNE Flux The intrinsic ne , ~0.5% of the neutrino flux, are one of the major backgrounds for nm  ne search. L(m), E(MeV), Dm2(eV2) Understanding Neutrino Flux (I):  Understanding Neutrino Flux (I) E910 @ BNL + previous world data fits Basis of current MiniBooNE p production model HARP @ CERN, 8 GeV Proton Beam MiniBooNE target slug - thin target (5, 50, 100 % l) Measure p+ production Understanding Neutrino Flux (II):  Understanding Neutrino Flux (II) Little Muon Counter (LMC) Scintillating fibre tracker 7 degrees off axis K decays produce wider angle m than p decays K production is deduced by measuring off-axis m The MiniBooNE Detector:  The MiniBooNE Detector 12m diameter tank Filled with 800 tons of pure mineral oil Optically isolated inner region with 1280 PMTs Outer veto region with 240 PMTs. PMT:  PMT Delayed Scintillation Energy Calibration:  Energy Calibration  Michel e from  decay: low energy 52.8 MeV. cosmic ray  + tracker + cubes: calibrate m energy ranging from 100 ~ 800 MeV 0 mass peak: calibrate medium energy, photons decay from 0 ranging 50 ~ 400 MeV Michel e: sE~15% p0: smp0~20 MeV/c2 Neutrino Candidates:  Neutrino Candidates DAQ triggered on beam from Booster Detector read out for 19.2 ms Neutrino pulse through detector lasts 1.6 ms 1.09 neutrino candidates / 1E15 POT With a few very simple cuts (time window, tank/veto hits) to obtain pure neutrino events. Event Topology:  Event Topology Event Reconstruction:  Event Reconstruction To reconstruct event position, direction, time, energy and invariant mass etc. Cerenkov light – prompt, directional Scintillation light – delayed, isotropic Using time likelihood and charge likelihood method to determine the optimal event parameters. Two parallel reconstruction packages S-Fitter is based on a simple, point-like light source model; P-Fitter differs from S-Fitter by using more 0th approximation tries, adding e/m tracks with longitudinally varying light source term, wavelength-dependent light propagation and detection, non-point-like PMTs and photon scattering, fluorescence and reflection. Particle Identification:  Particle Identification Two complementary and parallel methods: Log-likelihood technique: simple to understand, widely used in HEP data analysis Boosted Decision Trees: Non-linear combination of input variables Great performance for large number of input variables (about two hundred variables) Powerful and stable by combining many decision trees to make a 'majority vote' Boosted Decision Trees:  Boosted Decision Trees How to build a decision tree ? For each node, try to find the best variable and splitting point which gives the best separation based on Gini index. Gini_node = Weight_total*P*(1-P), P is weighted purity Criterion = Gini_father – Gini_left_son – Gini_right_son Variable is selected as splitter by maximizing the criterion. How to boost the decision trees? Weights of misclassified events in current tree are increased, the next tree is built using the same events but with new weights, Typically, one may build few hundred to thousand trees. How to calculate the event score ? For a given event, if it lands on the signal leaf in one tree, it is given a score of 1, otherwise, -1. The sum (probably weighted) of scores from all trees is the final score of the event. Performance vs Number of Trees:  Performance vs Number of Trees  Boosted decision trees focus on the misclassified events which usually have high weights after hundreds of tree iterations. An individual tree has a very weak discriminating power; the weighted misclassified event rate errm is about 0.4-0.45. The advantage of using boosted decision trees is that it combines many decision trees, 'weak' classifiers, to make a powerful classifier. The performance of boosted decision trees is stable after a few hundred tree iterations. Ref1: H.J.Yang, B.P. Roe, J. Zhu, 'Studies of Boosted Decision Trees for MiniBooNE Particle Identification', Physics/0508045, Nucl. Instrum. andamp; Meth. A 555(2005) 370-385. Ref2: B.P. Roe, H.J. Yang, J. Zhu, Y. Liu, I. Stancu, G. McGregor, 'Boosted decision trees as an alternative to artificial neural networks for particle identification', physics/0408124, NIMA 543 (2005) 577-584. Blindness Analysis:  Blindness Analysis We do not look into the data region where the ne oscillation candidates are expected. We are allowed to use part sample to check the goodness of Monte Carlo modeling Some of the information in all of the data All of the information in some of the data To use NuMI sample as an useful cross check To use nm background events to study the agreement of Data and Monte Carlo events. NuMI Sample:  NuMI Sample NuMI Sample:  NuMI Sample NuMI Sample:  NuMI Sample MiniBooNE Data VS. Monte Carlo:  MiniBooNE Data VS. Monte Carlo Outputs of Boosted Decision Trees Visible Energy, Tank Hits, Radius MiniBooNE Event Rates:  MiniBooNE Event Rates  n events currently 'on-tape' 5.6E20 protons-on-target (POT) Measurement of Dirt Events:  Measurement of Dirt Events Neutrino beam interacts with dirt outside of tank, the high energy photons (100 ~ 300 MeV) sneak into the tank to produce electron-like Cerenkov ring. N_dirt_measured / N_dirt_expected = 0.99 ± 0.15 Dirt events contribute ~10% of background for oscillation nue search. Event Type of Dirt after PID cuts Expected Nue Oscillation Events:  Expected Nue Oscillation Events N_oscnue ~ 239 (Dm2=1.0 eV2, sin22Q=0.004), N_background ~ 702, N_dirt ~ 80 + HE box data Nue Oscillation Sensitivity:  Nue Oscillation Sensitivity MiniBooNE aims to cover most of LSND allowed region at 90% CL. We are currently finalizing systematic error matrix from beam flux, cross sections, detector modeling, optical modeling etc. We are finalizing analysis program. We anticipate to open box shortly.

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